# # import the necessary packages
# from PIL import Image
# import pytesseract
# import argparse
# import cv2
# import os
#
# # construct the argument parse and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required=True,
#      help="path to input image to be OCR'd")
# ap.add_argument("-p", "--preprocess", type=str, default="thresh",
#      help="type of preprocessing to be done")
# args = vars(ap.parse_args())
#
# # load the example image and convert it to grayscale
# image = cv2.imread(args["image"])
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#
# # check to see if we should apply thresholding to preprocess the
# # image
# if args["preprocess"] == "thresh":
#     gray = cv2.threshold(gray, 0, 255,
#     cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
#
# # make a check to see if median blurring should be done to remove
# # noise
# elif args["preprocess"] == "blur":
#     gray = cv2.medianBlur(gray, 3)
#
# # write the grayscale image to disk as a temporary file so we can
# # apply OCR to it
# filename = "{}.png".format(os.getpid())
# cv2.imwrite(filename, gray)
#
# # load the image as a PIL/Pillow image, apply OCR, and then delete
# # the temporary file
# text = pytesseract.image_to_string(Image.open(filename))
# os.remove(filename)
# print(text)
#
# # show the output images
# cv2.imshow("Image", image)
# cv2.imshow("Output", gray)
# cv2.waitKey(0)


import pytesseract
from PIL import Image

# 打开图像：英文
image = Image.open('pics/FORTEERACT1.jpg')

# OCR识别：lang默认英文
text = pytesseract.image_to_string(image)

# 打印识别后的文本
print(text)

# 我是分割线
print("*" * 30)

# 打开图像：英文
image = Image.open('china.png')

# OCR识别：lang指定中文
text = pytesseract.image_to_string(image, lang='chi_sim')

# 打印识别后的文本
print(text)